Simulation Parameters Variable 1 1 1 Impulse Width 1 Variable 1 1 Number of Impulses 0 0 Variable 0 Balance Gap 0.9 and 1.9 0.9 and 1.9 0.9 and 1.9 Variable Impulse Amplitude 100 100 100 100 Mean Demand 2 2 2 2 Transportation Lead Time (Days) 2 2 2 2 Information Lead Time (Days) 90 90 90 90 Observation Period (Days) 20 20 20 20 Warmup Period (Days) 110 110 110 110 Run Length (Days) Step Width Number of Impulses Balance Gap Demand Amplitude Parameter
Each policy was balanced so that all of them gave same results for the test demand under steady state condition
Demand Flow: The test demand was a constant demand of 100 units per week. To fulfill the current obligations, each node has to keep a minimum of 100 units. Each node has to keep an initial inventory equal to four weeks of demand. As a result, an initial inventory of 400 units was allocated to each node.
Order Q: In this policy, orders are placed even when no there is no demand. Therefore, inventory builds up for each node, until the actual demand is received. As a result, all nodes only need to keep an inventory equal to the value of demand per week (100 units).
Postponement strategies for re-engineering of automotive manufacturing: knowledge-management implications , International Journal of Advanced Manufacturing Technology, Article in Press, doi 10.1007/s00170-006-0679-z.
Hybrid Tabu-Sample Sort Simulated Annealing (SSA) with Fuzzy Logic Controller: CIM System Context , Studies in Informatics and Control, June 2006, Volume 15, Number 2.
Flexible Supply Chains: A Context for Decision Knowledge Sharing and Decision Delays , Global Journal of Flexible Systems Management, Volume 7 Numbers 3 & 4, July -Dec 2006 (Accepted for Publication).
Impact of Supply Chain Collaboration on Customer Service Level and Working Capital , Global Journal of Flexible Systems Management (Accepted for Publication).
A multi-criteria customer allocation problem in supply chain environment: an artificial immune system with fuzzy logic controller based approach , International Journal of Computers Communication and Control.
Inventory performance of some supply chain inventory policies under impulse demands , International Journal of Production Research, Manuscript ID: TPRS-2007-IJPR-0111.
An Object Oriented Framework for Modeling Control Policies in a Supply Chain , International Journal of Value Chain Management
List of Publications National / International Conferences
Web Based Virtual Supply Chain Modeling to Enhance Learning , The International Conference on e-Learning (ICEL 2006), University of Quebec in Montreal, Canada, June 22-23.
Supply Chain Modeling: The agent based Approach , 12th IFAC Symposium on Information Control Problems in Manufacturing (INCOM-2006), Saint-Etienne, France, May 17-19.
Object-Oriented Approach for Simulation of Supply Chain , International Congress on Logistics and SCM Systems (ICLS-2006), Kaohsiung, Taiwan, May 1-2.
Comparison of some Supply Chain Management Software Applications , National Conference on Advances in Mechanical Engineering (AIME-2006), January 20-21.
If answers to (1) and (2) above is yes, then explain why optimization on IS level (information sharing) is necessary? Why would an intermediate value (of IS) would be optimal? Whose objective have you considered? Individual wholesalers/retailers or the whole system? Or a combination of the two?
It is important to find the level and type of information sharing
On page xxvii: IT should be information technology
The required change has been made.
Uncertainty in supply chain is demand side and the lead time size. When you consider disturbances: you could have considered lead time disturbances.
The current developed framework is limited to only two players, i.e. manufacturing and inventory … more than three players?
There are four players in the supply chain considered in this research: Retailer , Wholesaler , Distributor and Manufacturer
In addition to the inbuilt player roles like supplier, manufacturer, distributor, wholesaler and retailer, users can also define their own Player Roles .
Network manufacturing is a new arena for modern manufacturing environment. How could … contribution in this field?
For network manufacturing also, this framework can still handle the execution side
In network manufacturing, the manufacturing of the finished product takes place through a coordination of multiple autonomous players. Such a network will have most of the players as manufacturing type players.
This framework can be used where higher level modeling of the manufacturing system is sufficient
In this research, “overall supply chain cost” has been used as the major criterion for the supply chain performance. In fact, there are many Key Performance Indicators (KPI) reported in the supply chain management research work, such as agilability, lead time, flexibility, expandability, trust, etc. How could you consider these issues into your research framework?
The research framework, in its present form, has only the KPIs which were required for this research work, i.e. those related to inventory management
Since the framework is based on object oriented methodology, multiple KPI libraries can be added to it as and when need arises
What are the major bottlenecks in the implementation of the developed framework in real-life industrial case?
The framework has been developed considering a very generic nature of the supply chain
What are the major limitations of the developed framework in this thesis?
The return operation of a supply chain is not available in the framework.
In the future, some other major supply chain operations may be added in the framework.
The effectiveness of the simulation environment can be immensely improved by incorporating some optimization algorithms for simpler supply chain decisions and some meta-heuristics for complex problems.
Another important direction for future work is to provide animated simulation similar to that available in other simulation languages.